DataVis MakeOver 2

DataVis MakeOver 2 for Visual Analytics.

He Qiyun https://example.com/norajones (SMU MITB (AI))https://scis.smu.edu.sg/
2022-07-06

The completed design can be found at : https://public.tableau.com/app/profile/qiyun.he/viz/DataVisMakeOver2/Dashboard1

1. Data Visualization Critique

The original visualization aims to reveal inter- and intra-zonal public bus flows at the planning sub-zone level of January 2022.

original_design

A good visualization should be both clear and visually appealing.
Next section will focus on critiques of the original visualization based on clarity and aesthetics.

The overall approach is to use the four quadrant to critique the graphs, as well as including improvements that can be made to improve interactivity.

1.1. Critiques: Clarity

(a) Dashboard has no title

Without a proper title, user may find it confusing about what the dashboard is showing. As the dashboard is showing inter- and intra-zonal public flows at the planing sub-zone level of January 2022, it would be useful to add this information in the title.

(b) Improper labeling of x-axis for bar charts:

For ‘WEEKENDS/HOLIDAY’ label, only ‘WEEKENDS’ is shown. This makes the label not clear to the user that the day type is ‘WEEKENDS/HOLIDAY’ instead. This can be adjusted by rotating the label to vertical orientation or add the day type information in subtitle so that it is clear.

(b) Adjacency matrix showing too many values:

The two adjacency matrix are showing too many values. As the space on a dashboard is limited, the adjacency matrix cannot occupy too much space, making it confusing for user to make sense of the matrix. It would be clearer if can reduce the values shown on the matrix so that user can make sense of the matrix. For example, for a selected ‘Origin Sz’, it would be helpful to show trip amount just for this Subzone and corresponding destination Subzones as user can then analysis which destination Subzone attracted more trips from the selected origin Subzone.

(d) Different y axis values making it hard to make comparisons

As the y-axis values for bar charts are different, if user want to compare across bar charts, it will be confusing to them. This can be solved by adding a reference line to each bar chart showing the average trip amount.

1.2. Critiques: Aesthetics

(a) Color too dull for adjacency matrix

The grey color for both adjacency matrix is too dull and not visually appealing. Using different colors for each will be better.

(b) Single color for all bar charts

Only blue color is used for all bar charts, different colors can be used to better help user to differentiate weekdays and weekends/holiday.

(c) Subtitle does not stand out

Subtitles are not in bold, it will be better to format it as bold.

1.3. Critiques: Interactivity

(a) Not enough view manipulation for bar charts

The bar charts will change according to the ‘Origin Sz’ and ‘Destination Sz’ chosen. However, just showing the bars does not help user to identify points of interest. For example, use may want to look at the hours with more number of trips. Hence, it would be useful if user can select to check the top N hours with most number of trips.

(b) User input only affect of bar charts

When selecting ‘Origin Sz’ or ‘Destination Sz’, corresponding bar charts changed, but the adjacency matrix does not seem to change. Hence, it would be helpful to include other filters for adjacency matrix for user to interact with the display.

(c) Tooltip for adjacency matrix not clear

When hovering over the adjacency matrix, the tooltip shows values for ‘Origin Sz’, ‘Destination Sz’ and ‘% of Total Total Trips along Table (Across)’. However, the name for the last value is too long and can be confusion to the user. It would be better to just change it to ‘Total Trips Percent’.

Also, apart from percentage value, adding more information such as absolute value of bus trips can be helpful as it helps user to understand the magnitude of number of trips in which percentage is calculated upon.

(d) Single value list not convenient for user

As there are more than a hundred Subzones, it is inconvenient for use to acroll down the single value list and find the Subzone they want to inspect. Change the Single value list such that a Subzone be be found by typing its name will be more convenient for user.

2. Sketch of Proposed Design

Below shows a sketch of the proposed design.

3 Create graphs

This section will shows the detail process of creating the graphs.

3.1: Data used

Drag the data ’origin_destination_bus__SZ_202201.csv’ to Tableau. The data contains information about trip amount, day type, origin subzone and destination subzone etc.

3.1 Create the graphs

This section will describe the steps for creating the graphs. In total, there are four bar charts, two adjacency matrix and one dashboard that need to be created.

3.2.1. Create the first graph

3.2.2. Create the second graph

3.2.3. Create the third graph

3.2.4. Create the fourth graph

3.2.5. Create the fifth graph

3.2.6. Create the sixth graph:

3.2.7. Create the final dashboard:

4. Main Observations

Using the subzone ‘CHANGI WEST’ as an example input, we can observe the following:

1. Different average trip amount between different day types

From the graph, it can be observed that the trip amount for trips generated from or attracted to a particular subzone such as ‘CHANGI WEST’ is different between weekdays and weekends/holiday as shown by the different average trip amount on the reference line.

The trips generated from or attracted to ‘CHANGI WEST’ during weekends/holiday is generally less than that of weekdays. This makes sense as weekdays have longer duration.

2. Similar time in hour having top N amount of trips

Although the average difference between different day types are different, but the trend of trip amount over time in hours are similar for the same origins subzone or destination subzone. This means the top N hours arranged according to number of trip are also similar for both day types. This is observed in the particular example of ‘CHANGI WEST’ whereby the top 5 hours for both day types are similar.

3. A subzone can generates or attracts most of its trips from a few other subzones

Arranging the adjacency matrix by clicking on the button circled in red below, we can check the associated location with most number of trips.

This is supported by the particular example of ‘CHANGI WEST’, it has the 49.48% of trips to ‘TAMPINES EAST’,and 60.02% of trips from ‘TAMPINES EAST’. Although it has trips linking to many other subzones, but other subzones account for only around 10% or less than 10% of the trips.

5. References

Link for creating parameters : (https://www.tableau.com/learn/tutorials/on-demand/parameters?_ga=2.201039597.2110822164.1648026853-1224120818.1642181744)

Link for showing legend on dashboards: (https://kb.tableau.com/articles/howto/showing-legends-on-dashboards)

Calculate Percentages in Tableau: (https://help.tableau.com/current/pro/desktop/en-us/calculations_percentages_options.htm)

Parameters in Tableau: (https://interworks.com/blog/rcurtis/2016/06/02/tableau-deep-dive-parameters-filtering-across-data-sources/)